Methods and materials for assessing immune system profiles

ABSTRACT

This document provides methods and materials involved in assessing immune system profiles. For example, methods and materials for performing flow cytometry to determine the number of CD4 +  lymphocytes, CD8 +  lymphocytes, regulatory T cells, B cells, NK cells, granulocytes, CD14 + HLA-DR lo/neg  g monocytes, and/or CD86 +  monocytes per unit volume (e.g., cells per μL or mL) of whole blood (e.g., fresh, un-manipulated whole blood) obtained from a mammal (e.g., a human) are provided.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. application Ser. No.14/442,465, filed May 13, 2015, which is a National Stage applicationunder U.S.C. § 371 of International Application No. PCT/US2013/069573,filed Nov. 12, 2013, which claims the benefit of U.S. ProvisionalApplication Ser. No. 61/725,902, filed Nov. 13, 2012. The disclosures ofthe prior applications are considered part of (and are incorporated byreference in) the disclosure of this application.

BACKGROUND 1. Technical Field

This document relates to methods and materials involved in assessingimmune system profiles. For example, this document provides methods andmaterials for performing flow cytometry to determine the immune statusof a mammal (e.g., a human) using whole blood (e.g., fresh,un-manipulated whole blood) obtained from the mammal.

2. Background Information

The immune system of a mammal is a system of biological structures andprocesses that helps protect the mammal from diseases by identifying andkilling pathogens and tumor cells. It also plays a role in an organism'shomeostasis and in tissue healing and repair. The immune system is madeup of a combination of white blood cells (leukocytes) transportedthrough the body by blood. A monocyte is one type of white blood cellthat is part of the immune system. Monocytes can have several roles inthe immune system. For example, monocytes can migrate to sites ofinfection and differentiate into macrophages and dendritic cells.Another type of cell that is part of the immune system is a CD4⁺ T cell.CD4⁺ T cells are a sub-group of lymphocytes that help activate anddirect other cells of the immune system.

SUMMARY

This document provides methods and materials involved in assessingimmune system profiles. For example, this document provides methods andmaterials for performing flow cytometry to determine the immune statusof a mammal (e.g., a human) using whole blood (e.g., fresh,un-manipulated whole blood) obtained from the mammal.

In some cases, the immune status of a mammal is determined by measuring,for example, the number of CD4⁺ lymphocytes, CD8⁺ lymphocytes,regulatory T cells, B cells, NK cells, granulocytes,CD14⁺HLA-DR^(lo/neg) CD86⁺ monocytes per unit volume (e.g., cells per μLor mL). The immune status can be determined by quantitatingrepresentatives of each major category of leukocytes (e.g.,granulocytes, NK cells, T cells, B cells, myeloid, lymphocytes etc.) andcomparing the amount and distribution of these cells to the numbersfound in a database of similar measurements from a population ofindividuals. The comparison to this database can be used to classify themammal as having a particular immune system profile (e.g., immune systemprofile 1, immune system profile 2, immune system profile 3, immunesystem profile 4, or immune system profile 5). Immune system profilescan be used to identify appropriate therapies for different pathologies.

In some cases, a method provided herein for determining the immunestatus of a mammal can include three steps. First, the measurement ofleukocyte subtypes in a manner that allows the number of cells per unitvolume such as the number of a subtype per μL of blood volume can bedetermined. This can be an individual immune phenotype. Second, acollection of a sufficient number of individual immune phenotypes thatare together within a mammal can be used to generate a database. Thedatabase can then be analyzed to segregate like immune phenotypes usingan analysis tool that can perform similarity analyses (e.g.,hierarchical clustering or PCA analysis). Clusters of immune phenotypescan be considered immune profiles. Third, once the immune profiles arecharacterized, an individual immune phenotype can be compared to thedatabase to identify the underlying profile that individual belongs to.Immune profiles can be used to predict response to therapy, identifysubtypes that correlate (or inversely correlate), or diagnosepathological subtypes.

In some cases, this document provides methods and materials forperforming flow cytometry to determine an immune phenotype that includesthe number of CD4⁺ lymphocytes, CD8⁺ lymphocytes, regulatory T cells, Bcells, NK cells, granulocytes, CD14⁺HLA-DR^(lo/neg) monocytes, and/orCD86⁺ monocytes per unit volume (e.g., cells per μL or mL) of wholeblood (e.g., fresh, un-manipulated whole blood) obtained from a mammal(e.g., a human). The numbers of such cells per unit volume can becompared to immune profile reference information within a databaseobtained from similarly screened controls. Based at least in part on thecomparison information within the database, the mammal can be identifiedas having a particular immune system profile (e.g., immune systemprofile 1, immune system profile 2, immune system profile 3, immunesystem profile 4, or immune system profile 5).

As described herein, fresh, un-manipulated whole blood obtained from amammal (e.g., a human) can be assessed using flow cytometry to determinethe number of one or more (e.g., two, three, four, five, six, seven, oreight) of the following cell types per unit volume (e.g., μL): CD4⁺lymphocytes, CD8⁺ lymphocytes, regulatory T cells, B cells, NK cells,granulocytes, CD14⁺HLA-DR^(lo/neg) monocytes, and CD86⁺ monocytes. Forexample, fresh, un-manipulated whole blood obtained from a human can beassessed using flow cytometry to determine the number CD4⁺ lymphocytes,CD8⁺ lymphocytes, regulatory T cells, B cells, NK cells, granulocytes,CD14⁺HLA-DR^(lo/neg) monocytes, and CD86⁺ monocytes per μL. Such cellnumbers can be used to classify the mammal as having a particular immunesystem profile (e.g., immune system profile 1, immune system profile 2,immune system profile 3, immune system profile 4, or immune systemprofile 5) by, for example, identifying individuals whose immune systemscluster together. Mammals having immune system profile 1 or 2 can behealthy or can be likely to have a favorable medical outcome for aparticular medical condition (e.g., cancer, autoimmunity, sepsis, woundhealing, or infection). Mammals having other immune system profiles(e.g., immune system profiles 3, 4, or 5) can be likely to have a poorermedical outcome for a particular medical condition (e.g., cancer,sepsis, autoimmunity, wound healing, or infection). The methods andmaterial provided herein can be used to identify immune profiles thatmay react consistently within a profile even in the presence of adifferent underlying pathology. Thus, immune modulating therapies can betested on each immune profile, and the results logically extended toother immune profiles. The ability to identify the medical outcome asdescribed herein can be related to the size of the comparative database.Sufficient database size can allow differential outcomes to bedetermined for each individual profile.

The methods and materials provided herein can allow clinicians toprovide patients with information about the state of their immune systemand likely outcomes for various medical conditions. In some cases, suchinformation can allow clinicians and patients to determine propertreatment options. For example, the methods and materials providedherein can be used to develop or select appropriate treatments forcancer patients.

In general, one aspect of this document features a method for treating ahuman having glioblastoma. The method comprises, or consists essentiallyof, (a) performing flow cytometry using a blood sample obtained from ahuman having glioblastoma to identify the human as having an immunesystem profile of a population of healthy humans, and (b) administeringsurgery, radiation, or chemotherapy to the human. The method cancomprise performing flow cytometry using the blood sample to identifythe human as having the immune system profile.

In another aspect, this document features a method for treating a humanhaving renal cell carcinoma. The method comprises, or consistsessentially of, (a) performing flow cytometry using a blood sampleobtained from a human having renal cell carcinoma to identify the humanas having an immune system profile of a population of healthy humans,and (b) administering surgery, IL-2, or cryotherapy to the human. Themethod can comprise performing flow cytometry using the blood sample toidentify the human as having the immune system profile.

In another aspect, this document features a method for treating a humanhaving non-Hodgkin lymphoma, wherein the method comprises, or consistsessentially of, (a) performing flow cytometry using a blood sampleobtained from a human having non-Hodgkin lymphoma to identify the humanas having an immune system profile of a population of healthy humans,and (b) administering CHOP, RCHOP, or radiotherapy to the human. Themethod can comprise performing flow cytometry using the blood sample toidentify the human as having the immune system profile.

In another aspect, this document features a method for determining theimmune system profile of a human, wherein the method comprises, orconsists essentially of, (a) performing flow cytometry using whole bloodobtained from a human to determine the numbers of CD4⁺ lymphocytes, CD8⁺lymphocytes, regulatory T cells, B cells, NK cells, granulocytes,CD14⁺HLA-DR^(lo/neg) monocytes, and CD86⁺ monocytes per unit volume ofthe whole blood, (b) comparing the numbers to information within adatabase, wherein the database comprises the numbers of CD4⁺lymphocytes, CD8⁺ lymphocytes, regulatory T cells, B cells, NK cells,granulocytes, CD14⁺HLA-DR^(lo/neg) monocytes, and CD86⁺ monocytes perunit volume present within a population of healthy humans and apopulation of humans with a medical condition having a known outcome forthe medical condition, and (c) classifying the human as having an immunesystem profile comparable to that of at least one collection of memberswithin the database. The human can be classified as having immune systemprofile of a population of healthy humans. The whole blood can be afresh, un-manipulated whole blood sample obtained from the human.

In another aspect, this document features a method for assessing thelikelihood that a mammal having a medical condition will experience afavorable or unfavorable outcome, wherein the method comprises, orconsists essentially of, (a) performing flow cytometry to determine if awhole blood obtained from a human contains a CD4⁺/CD14⁺HLA-DR^(lo/neg)ratio greater than or less than 2, (b) classifying the mammal as beinglikely to experience a favorable outcome of the medical condition if thewhole blood contains a CD4⁺/CD14⁺HLA-DR^(lo/neg) ratio greater than 2,and (c) classifying the mammal as being likely to experience anunfavorable outcome of the medical condition if the whole blood containsa CD4⁺/CD14⁺HLA-DR^(lo/neg) ratio less than 2. The mammal can be ahuman. The medical condition can be cancer. The cancer can beglioblastoma. The whole blood can contain a CD4⁺/CD14⁺HLA-DR^(lo/neg)ratio greater than 2, and wherein the method can comprise classifyingthe mammal as being likely to experience a favorable outcome. Thefavorable outcome can comprise surviving the glioblastoma for more than600 days. The whole blood can contain a CD4⁺/CD14⁺HLA-DR^(lo/neg) ratioless than 2, and wherein the method can comprise classifying the mammalas being likely to experience an unfavorable outcome. The unfavorableoutcome can comprise surviving the glioblastoma for less than 400 days.The cancer can be a lymphoma. The whole blood can contain aCD4⁺/CD14⁺HLA-DR^(lo/neg) ratio greater than 2, and wherein the methodcan comprise classifying the mammal as being likely to experience afavorable outcome. The favorable outcome can comprise surviving thelymphoma for greater than 750 days. The whole blood can contain aCD4⁺/CD14⁺HLA-DR^(lo/neg) ratio less than 2, and wherein the method cancomprise classifying the mammal as being likely to experience anunfavorable outcome. The unfavorable outcome can comprise a likelihoodof not surviving the lymphoma for greater than 750 days.

In another aspect, this document features the use of an immune profileto speed test immune modulating drugs by testing drugs based on patientswith the same profile and using that to treat other patients with thesame profile but a different underlying pathology.

In another aspect, this document features the use of an immune profileto determine when to stop dosing for immune modulating drugs. In somecases, a drug can continue to be dosed to solicit a change in an immuneprofile until the profile changes to that of a healthy individual.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention pertains. Although methods and materialssimilar or equivalent to those described herein can be used to practicethe invention, suitable methods and materials are described below. Allpublications, patent applications, patents, and other referencesmentioned herein are incorporated by reference in their entirety. Incase of conflict, the present specification, including definitions, willcontrol. In addition, the materials, methods, and examples areillustrative only and not intended to be limiting.

The details of one or more embodiments of the invention are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages of the invention will be apparent from thedescription and drawings, and from the claims.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is graph plotting the age of subjects by disease group. *indicates p value<0.05 as compared to healthy volunteers (HV).

FIGS. 2A-2E are graphs of hierarchical clustering identifying immuneprofiles within patient groups. Peripheral blood leukocyte populationswere measured by flow cytometry. The number of cells/μL for each markerwas determined directly or converted from TruCountTM tubes. Allphenotype values were normalized against the mean of similarly measuredand converted healthy volunteers (n=40). Unsupervised clustering wasperformed using ten immune markers. The same HV cohort was used for allclustering analysis. Identification of major clusters is indicated atleft. A row represents one subject, and a column represents one of tenmarkers measured. FIG. 2A contains the clustering of patients withglioblastoma (GBM; n=27). GBM patients were further identified based onthe presences of pre-operative dexamethasone (a known immune suppressor)or its absence. The results of the clustering are included in tabularform demonstrating the relationship between the identified profile andthe underlying disease. FIG. 2B contains the clustering of patients withnon-Hodgkin lymphoma (NHL; n=28). The tabular results from theclustering are included showing the number of healthy volunteers and NHLpatients in each of the profiles. FIG. 2C contains the clustering ofrenal cell carcinoma patients (RCC; n=25). The tabular results from theclustering are included showing the number of healthy volunteers and RCCpatients in each of the profiles. FIG. 2D contains the clustering ofovarian cancer patients (OVA; n=17). The tabular results from theclustering are included showing the number of healthy volunteers and OVApatients in each of the profiles. FIG. 2E contains the clustering ofpatients with acute lung injury with or at risk for sepsis (ALI; n=23).ALI patients were further identified as those with or without confirmedsepsis as well as those that did or did not survive the episode. Thetabular results from the clustering are included showing the number ofhealthy volunteers and ALI (with and without sepsis) patients in each ofthe profiles.

FIGS. 3A and 3B contains data of distinct immune profiles that areshared across patient populations. Ten immune markers for eachindividual from healthy volunteers (n=40) and patients (n=120) were usedas sample data for combined clustering analysis. FIG. 3A is ahierarchical clustering dendrogram of patients and HV. Profiles wereassigned based on the separation of the clustering trees. FIG. 3B is atabular representation of the demographics of the patients within eachprofile. The composition of the profile is dependent on the memberstested. The profiles determined within a patient specific analysis donot correspond to the profiles identified with the whole group.

FIG. 4A-4C demonstrates that the immune profiles are distinct inrelative and absolute composition of immune markers. Immune markers fromeach subject in a designated profile were evaluated for statisticalsignificance. FIG. 4A contains graphs comparing immune marker cellcounts. Box and whisker plots show mean, maximum, and minimum values foreach data set. Box represents the 25th to 75th percentile range.HV=healthy volunteers only. *=p<0.05 and **=p<0.0001. Each profile wascompared to the healthy volunteer cohort. FIG. 4B contains graphscomparing immune marker percentages. Box and whisker plots show mean,maximum, and minimum values for each data set. Box represents the 25thto 75th percentile range. HV=healthy volunteers only. *=p<0.05 and**=p<0.0001. Each profile was compared to the healthy volunteer cohort.FIG. 4C contains pie graph visualizations of immune profile size andcomposition. To develop a picture of the composition of circulatingpopulations, selected immune markers (in cells/μL) were totaled, and themeans were totaled within a profile. The average profile was used toreconstruct the exemplar within each profile. Graph size representstotal leukocytes/μL for the average profile relative to the average ofProfile 1. Graphs on the left show the three major components ofleukocytes. Graphs on the right show selected proportions of mononuclearcells.

FIG. 5 contains graphs comparing total leukocyte and mononuclear cellcounts (cells/μL) across profiles.

FIG. 6 contains survival data for GBM, NHL, and RCC patients based onimmune profiles. Individual patients with GBM, NHL, or RCC with survivaldata were assigned a profile from FIG. 3. The survival data was adjustedto remove contributions of age and disease. Profiles 1 and 2 weregrouped as they represent the only profiles seen in healthy volunteersand were compared to the survival of patients with profiles of 3, 4, and5. Survival data were plotted for each immune profile regardless ofunderlying disease. P values were calculated by the Mantel-Cox log ranktest.

FIGS. 7A-D are graphs plotting the survival of patients categorized byimmune profile in each indicated disease group.

FIGS. 8A-8C contain data of hierarchical clustering identifyingrelationships between immune markers. FIG. 8A. An additional 13 immunemarkers were added to the original ten. Cancer patients (n=48) andhealthy volunteers (n=31) in a subset of patients and analyzed as inFIG. 2. White boxes in dendrogram indicate that data was not collectedor deemed suitable for analysis. For correlative studies, values fromall 160 healthy volunteers and patients were used. FIG. 8B contains dataof monocytes or granulocytes plotted against CD14⁺HLA-DR^(lo/neg)monocyte cell counts, and CD4 T cell counts plotted against thepercentage of CD14⁺HLA-DR^(lo/neg) monocytes of total CD14⁺ monocytes. Pvalues were calculated using the Spearman non-parametric correlationtest. FIG. 8C is a graph plotting overall survival for GBM, NHL, and RCCpatients adjusted for age and disease. A ratio of cells/μL of CD4 T cellto CD14⁺HLA-DR^(lo/neg) monocytes was calculated for each patient andsubgrouped into those above or below a cut-off value of 2.0. Patientswith ratio at or above 2.0 (similar to healthy volunteers; dashed line)had a median overall survival of 30 months, and those below 2.0 (solidline) had a median overall survival of 9 months.

FIG. 9 contains tables listing examples of immunophenotyping panels thatcan be used as described herein.

DETAILED DESCRIPTION

This document provides methods and materials related to assessing immunesystem profiles. For example, this document provides methods andmaterials for performing flow cytometry to determine the number ofleukocyte subsets in circulation. These subsets can include the numberof CD4⁺ lymphocytes, CD8⁺ lymphocytes, regulatory T cells, B cells, NKcells, granulocytes, lymphocytes, monocytes, T cells,CD14⁺HLA-DR^(lo/neg) monocytes, and/or CD86⁺ monocytes per unit volume(e.g., cells per μL or mL) of whole blood (e.g., fresh, un-manipulatedwhole blood) obtained from a mammal (e.g., a human). After generatinginformation about the number of leukocyte subsets within a mammal, thatinformation can be included in a database along with similar informationobtained from a population of mammals (e.g., healthy mammals and mammalswith particular diseases or illnesses). Clustering algorithms can beused before or after normalization of the information within thedatabase. Typically, normalization can allow for changes of eachvariable independent of the value of that variable. This analysis can bea hierarchical clustering analysis, a PCA analysis, or any analysis thatgroups individuals based on like expression values (or correlativevalues). Any future individual(s) so typed can be compared to thedatabase individually or in groups. The clustering can produceindividuals alike across all parameters and identify the group (orimmune profile) that an individual belongs to.

As described herein, a mammal assigned to an immune profile can beexpected to have an immune system respond similarly to other memberswith the same immune profile. For example, an individual with cancerbelonging to a normal immune profile can be expected to respond toimmune modulating therapies similar to a healthy person treated in thesame manner.

As described herein, a human can have and/or can be classified as havinga healthy immune system profile when whole blood from that humancontains, per μL, from about 2500 to about 6500 granulocytes (e.g., fromabout 3000 to about 6000, from about 3500 to about 5500, or from about4000 to about 5000 granulocytes), from about 1000 to about 2500lymphocytes (e.g., from about 1250 to about 2250, from about 1500 toabout 2000, or from about 1600 to about 1800 lymphocytes), from about300 to about 700 monocytes (e.g., from about 350 to about 650, fromabout 400 to about 600, or from about 450 to about 550 monocytes), fromabout 800 to about 1700 T cells (e.g., from about 925 to about 1575,from about 1050 to about 1450, or from about 1100 to about 1400 Tcells), from about 115 to about 400 B cells (e.g., from about 150 toabout 350, from about 175 to about 325, or from about 200 to about 300 Bcells), from about 100 to about 350 NK cells (e.g., from about 125 toabout 325, from about 150 to about 300, or from about 175 to about 275NK cells), from about 550 to about 1400 CD4⁺ lymphocytes (e.g., fromabout 600 to about 1350, from about 650 to about 1300, or from about 700to about 1250 CD4⁺ lymphocytes), from about 10 to about 50 regulatory Tcells (e.g., from about 15 to about 45, from about 20 to about 40, orfrom about 22 to about 34 regulatory T cells), from about 15 to about 90CD14⁺HLA-DR^(lo/neg) monocytes (e.g., from about 25 to about 80, fromabout 35 to about 70, or from about 45 to about 57 CD14⁺HLA-DR^(lo/neg)monocytes), and from about 300 to about 600 CD86⁺ monocytes (e.g., fromabout 340 to about 560, from about 380 to about 520, or from about 420to about 480 CD86⁺ monocytes). In some cases, a human can have and/orcan be classified as having a healthy immune system profile when wholeblood from that human contains the cell counts as set forth in Table 7for healthy volunteers.

A mammal can have and/or can be classified as having an immune systemprofile 1 when whole blood from that mammal contains, per μL, from about3000 to about 9000 granulocytes (e.g., from about 3750 to about 8250,from about 4500 to about 7500, or from about 5250 to about 6750granulocytes), from about 950 to about 2500 lymphocytes (e.g., fromabout 1000 to about 2250, from about 1250 to about 2000, or from about1400 to about 1800 lymphocytes), from about 350 to about 750 monocytes(e.g., from about 375 to about 700, from about 425 to about 600, or fromabout 500 to about 560 monocytes), from about 650 to about 1750 T cells(e.g., from about 800 to about 1600, from about 950 to about 1450, orfrom about 1100 to about 1300 T cells), from about 50 to about 400 Bcells (e.g., from about 100 to about 350, from about 150 to about 300,or from about 200 to about 250 B cells), from about 100 to about 300 NKcells (e.g., from about 125 to about 275, from about 150 to about 250,or from about 175 to about 225 NK cells), from about 500 to about 1175CD4⁺ lymphocytes (e.g., from about 590 to about 1085, from about 680 toabout 995, or from about 770 to about 905 CD4⁺ lymphocytes), from about5 to about 55 regulatory T cells (e.g., from about 10 to about 50, fromabout 15 to about 45, or from about 20 to about 40 regulatory T cells),from about 10 to about 185 CD14⁺HLA-DR^(lo/neg) monocytes (e.g., fromabout 365 to about 610, from about about 145, or from about 70 to about125 CD14⁺HLA-DR^(lo/neg) monocytes), and from about 325 to about 650CD86⁺ monocytes (e.g., from about 365 to about 610, from about 405 toabout 570, or from about 445 to about 530 CD86⁺ monocytes). In somecases, a human can have and/or can be classified as having an immunesystem profile 1 when whole blood from that human contains the cellcounts as set forth in Table 7 for profile 1.

A mammal can have and/or can be classified as having an immune systemprofile 2 when whole blood from that mammal contains, per μL, from about550 to about 10500 granulocytes (e.g., from about 1800 to about 9250,from about 3050 to about 8000, or from about 4300 to about 6750granulocytes), from about 1300 to about 2600 lymphocytes (e.g., fromabout 1450 to about 2450, from about 1600 to about 2300, or from about1750 to about 2150 lymphocytes), from about 250 to about 500 monocytes(e.g., from about 280 to about 470, from about 310 to about 440, or fromabout 340 to about 410 monocytes), from about 870 to about 1920 T cells(e.g., from about 1000 to about 1790, from about 1130 to about 1660, orfrom about 1260 to about 1530 T cells), from about 90 to about 360 Bcells (e.g., from about 125 to about 325, from about 160 to about 290,or from about 195 to about 255 B cells), from about 40 to about 615 NKcells (e.g., from about 110 to about 545, from about 180 to about 475,or from about 250 to about 405 NK cells), from about 600 to about 1200CD4⁺ lymphocytes (e.g., from about 670 to about 1130, from about 740 toabout 1060, or from about 810 to about 990 CD4⁺ lymphocytes), from about15 to about 45 regulatory T cells (e.g., from about 19 to about 41, fromabout 23 to about 37, or from about 27 to about 32 regulatory T cells),from about 3 to about 17 CD14⁺HLA-DR^(lo/neg) monocytes (e.g., fromabout 5 to about 15, from about 7 to about 13, or from about 9 to about11 CD14⁺HLA-DR^(lo/neg) monocytes), and from about 230 to about 440CD86⁺ monocytes (e.g., from about 255 to about 415, from about 280 toabout 390, or from about 305 to about 365 CD86⁺ monocytes). In somecases, a human can have and/or can be classified as having an immunesystem profile 2 when whole blood from that human contains the cellcounts as set forth in Table 7 for profile 2.

A mammal can have and/or can be classified as having an immune systemprofile 3 when whole blood from that mammal contains, per μL, from about5350 to about 20250 granulocytes (e.g., from about 7200 to about 18400,from about 9050 to about 16550, or from about 10900 to about 14700granulocytes), from about 1500 to about 3000 lymphocytes (e.g., fromabout 1680 to about 2820, from about 1860 to about 2640, or from about2040 to about 2460 lymphocytes), from about 710 to about 1460 monocytes(e.g., from about 800 to about 1370, from about 890 to about 1280, orfrom about 980 to about 1190 monocytes), from about 1040 to about 2320 Tcells (e.g., from about 1200 to about 2160, from about 1360 to about2000, or from about 1520 to about 1840 T cells), from about 130 to about560 B cells (e.g., from about 180 to about 510, from about 230 to about460, or from about 280 to about 410 B cells), from about 90 to about 340NK cells (e.g., from about 120 to about 310, from about 150 to about280, or from about 180 to about 250 NK cells), from about 740 to about1660 CD4⁺ lymphocytes (e.g., from about 850 to about 1550, from about960 to about 1440, or from about 1070 to about 1330 CD4⁺ lymphocytes),from about 20 to about 90 regulatory T cells (e.g., from about 28 toabout 82, from about 36 to about 74, or from about 44 to about 66regulatory T cells), from about 280 to about 910 CD14⁺HLA-DR^(lo/neg)monocytes (e.g., from about 360 to about 830, from about 440 to about750, or from about 520 to about 670 CD14⁺HLA-DR^(lo/neg) monocytes), andfrom about 600 to about 1320 CD86⁺ monocytes (e.g., from about 690 toabout 1230, from about 780 to about 1140, or from about 870 to about1050 CD86⁺ monocytes). In some cases, a human can have and/or can beclassified as having an immune system profile 3 when whole blood fromthat human contains the cell counts as set forth in Table 7 for profile3.

A mammal can have and/or can be classified as having an immune systemprofile 4 when whole blood from that mammal contains, per μL, from about5900 to about 18900 granulocytes (e.g., from about 7520 to about 17280,from about 1940 to about 15660, or from about 10760 to about 14040granulocytes), from about 515 to about 1020 lymphocytes (e.g., fromabout 575 to about 960, from about 635 to about 900, or from about 695to about 840 lymphocytes), from about 370 to about 1200 monocytes (e.g.,from about 470 to about 1100, from about 570 to about 1000, or fromabout 670 to about 900 monocytes), from about 340 to about 790 T cells(e.g., from about 395 to about 735, from about 450 to about 680, or fromabout 505 to about 625 T cells), from about 30 to about 190 B cells(e.g., from about 50 to about 170, from about 70 to about 150, or fromabout 90 to about 130 B cells), from about 20 to about 170 NK cells(e.g., from about 40 to about 150, from about 60 to about 130, or fromabout 80 to about 110 NK cells), from about 190 to about 510 CD4⁺lymphocytes (e.g., from about 230 to about 470, from about 270 to about430, or from about 310 to about 390 CD4⁺ lymphocytes), from about 5 toabout 18 regulatory T cells (e.g., from about 7 to about 16, from about9 to about 14, or from about 11 to about 12 regulatory T cells), fromabout 100 to about 750 CD14⁺HLA-DR^(lo/neg) monocytes (e.g., from about180 to about 670, from about 260 to about 590, or from about 340 toabout 510 CD14⁺HLA-DR^(lo/neg) monocytes), and from about 200 to about1020 CD86⁺ monocytes (e.g., from about 300 to about 920, from about 400to about 820, or from about 500 to about 720 CD86⁺ monocytes). In somecases, a human can have and/or can be classified as having an immunesystem profile 4 when whole blood from that human contains the cellcounts as set forth in Table 7 for profile 4.

A mammal can have and/or can be classified as having an immune systemprofile 5 when whole blood from that mammal contains, per μL, from about3300 to about 10000 granulocytes (e.g., from about 4100 to about 9200,from about 4900 to about 8400, or from about 5700 to about 7600granulocytes), from about 200 to about 1100 lymphocytes (e.g., fromabout 300 to about 1000, from about 400 to about 900, or from about 500to about 800 lymphocytes), from about 100 to about 400 monocytes (e.g.,from about 140 to about 360, from about 180 to about 320, or from about220 to about 280 monocytes), from about 50 to about 850 T cells (e.g.,from about 150 to about 750, from about 250 to about 650, or from about350 to about 550 T cells), from about 0 to about 200 B cells (e.g., fromabout 20 to about 180, from about 40 to about 160, or from about 60 toabout 140 B cells), from about 20 to about 200 NK cells (e.g., fromabout 40 to about 180, from about 60 to about 160, or from about 80 toabout 140 NK cells), from about 0 to about 900 CD4⁺ lymphocytes (e.g.,from about 120 to about 780, from about 240 to about 660, or from about360 to about 540 CD4⁺ lymphocytes), from about 0 to about 45 regulatoryT cells (e.g., from about 6 to about 39, from about 12 to about 33, orfrom about 18 to about 27 regulatory T cells), from about 10 to about140 CD14⁺HLA-DR^(lo/neg) monocytes (e.g., from about 25 to about 125,from about 40 to about 110, or from about 55 to about 95CD14⁺HLA-DR^(lo/neg) monocytes), and from about 40 to about 350 CD86⁺monocytes (e.g., from about 80 to about 310, from about 120 to about270, or from about 160 to about 230 CD86⁺ monocytes). In some cases, ahuman can have and/or can be classified as having an immune systemprofile 5 when whole blood from that human contains the cell counts asset forth in Table 7 for profile 5.

In some cases, a mammal (e.g., a human) can be determined to have and/orcan be classified as having an immune system profile 1, 2, 3, 4, or 5 bydetermining the ratio of (a) the number of CD4⁺ lymphocytes, CD8⁺lymphocytes, regulatory T cells, B cells, NK cells, granulocytes,lymphocytes, monocytes, T cells, CD14⁺HLA-DR^(lo/neg) monocytes, and/orCD86⁺ monocytes present within a unit volume (e.g., μL) of whole blood(e.g., fresh, un-manipulated whole blood) from the mammal being assessedto (b) the number of CD4⁺ lymphocytes, CD8⁺ lymphocytes, regulatory Tcells, B cells, NK cells, granulocytes, lymphocytes, monocytes, T cells,CD14⁺HLA-DR^(lo/neg) monocytes, and/or CD86⁺ monocytes present within aunit volume (e.g., μL) of whole blood (e.g., fresh, un-manipulated wholeblood) from a population of healthy controls. For example, a human canhave and/or can be classified as having an immune system profile 1 or 2when whole blood from that human contains the cell counts as a ratio tothe cell counts of a healthy human as set forth in Table 8 for profile 1or 2.

The number of CD4⁺ lymphocytes, CD8⁺ lymphocytes, regulatory T cells, Bcells, NK cells, granulocytes, lymphocytes, monocytes, T cells, CD14⁺HLA-DR^(lo/neg) monocytes, and/or CD86⁺ monocytes present within a unitvolume (e.g., μL) of whole blood (e.g., fresh, un-manipulated wholeblood) can be determined using flow cytometry. For example, anti-CD4,anti-CD8, anti-CD86, anti-CD14, and anti-HLA-DR antibodies as well asthe antibodies and reagents listed in Table 3 can be used to performflow cytometry in order to determine the number of CD4⁺ lymphocytes,CD8⁺ lymphocytes, regulatory T cells, B cells, NK cells, granulocytes,lymphocytes, monocytes, T cells, CD14⁺HLA-DR^(lo/neg) monocytes, andCD86⁺ monocytes present within a unit volume (e.g., μL) of whole blood.In some cases, immunological techniques such as ELISA or cell stainingtechniques can be used to determine the number of CD4⁺ lymphocytes, CD8⁺lymphocytes, regulatory T cells, B cells, NK cells, granulocytes,lymphocytes, monocytes, T cells, CD14⁺HLA-DR^(lo/neg) monocytes, and/orCD86⁺ monocytes present within a unit volume (e.g., μL) of whole blood(e.g., fresh, un-manipulated whole blood). In some cases, techniquessuch as polymerase chain reaction or array technologies can be used todetermine the number of CD4⁺ lymphocytes, CD8⁺ lymphocytes, regulatory Tcells, B cells, NK cells, granulocytes, lymphocytes, monocytes, T cells,CD14⁺HLA-DR^(lo/neg) monocytes, and/or CD86⁺ monocytes present within aunit volume (e.g., μL) of whole blood (e.g., fresh, un-manipulated wholeblood).

Mammals (e.g., humans) having immune system profile 1 or 2 can behealthy or can be likely to have a favorable medical outcome for aparticular medical condition (e.g., cancer, autoimmunity, sepsis orwound healing). For example, a human diagnosed with cancer anddetermined to have immune system profile 1 as described herein can beclassified as being likely to have a favorable medical outcome for thatmedical condition.

In some cases, the methods and materials provided herein can be used toassociate the effect of immune modulating drugs with their effect on aparticular mammal having a pre-determined immune profile independent ofthe underlying disease pathology.

This document also provides methods for treating mammals. For example, ahuman having glioblastoma can be treated by (a) performing flowcytometry using a blood sample obtained from that human to identify thehuman as having an immune system profile 1 or 2, and (b) administeringsurgery, radiation, or temazolimide to the human. In another example, ahuman having renal cell carcinoma can be treated by (a) performing flowcytometry using a blood sample obtained from that human to identify thehuman as having an immune system profile 1 or 2, and (b) administeringsurgery, IL-2, or cryotherapy. In another example, a human havingnon-Hodgkin lymphoma can be treated by (a) performing flow cytometryusing a blood sample obtained from that human to identify the human ashaving an immune system profile 1 or 2, and (b) administering CHOP,RCHOP, or radiotherapy to the human.

The invention will be further described in the following examples, whichdo not limit the scope of the invention described in the claims.

EXAMPLES Example 1 Peripheral Blood Immune Phenotypes and MultiparameterAnalysis Reveal Prognostic Immune Profiles Independent of UnderlyingCancer Diagnosis Patients and Healthy Volunteers

Samples were collected, and previous results from typing peripheralblood for some glioblastoma (GBM) patients (Gustafson et al., Neuro.Oncol., 12:631 (2010)), non-Hodgkin lymphoma (NHL) patients (Lin et al.,Blood, 117:872 (2011)), and healthy volunteers were used for reanalysisin this study. Briefly, GBM patient samples were collected prior tosurgery with or without concurrent steroids. NHL patients were newlydiagnosed or recently relapsed patients off all chemotherapy for atleast eight weeks. Patients with metastatic renal cell carcinoma (RCC)were newly diagnosed or had recent relapsed disease with samples takenbefore cytoreductive nephrectomy. Ovarian cancer patients were newlydiagnosed or relapsed with no chemotherapy for the prior eight weeks.Specific characteristics are listed in Tables 1 and 2. Acute lung injurypatients who presented with at least one risk factor for acute lunginjury/acute respiratory distress syndrome (Iscimen et al., Crit. CareMed., 36:1518 (2008)) within 12 hours of admission and/or recognition ofthe diagnosis were selected. Inclusion criteria were used as describedaccording to the American-European Consensus Conference (Bernard et al.,Am. J. Crit. Care Med., 20:225 (1994)) and consisted of acute onset ofhypoxemia (where PaO₂/FiO₂≤300 is acute lung injury; where ≤200 is acuterespiratory distress syndrome) and diffuse radiologic infiltrates in theabsence of left atrial hypertension. Risk factors included pneumonia,sepsis, pancreatitis, shock, aspiration, high risk surgery, and highrisk trauma (Bauer et al., Intensive Care Med., 37:721 (2011)). The ageof the healthy volunteers was not different from each group except forRCC (FIG. 1).

TABLE 1 Overall Survival Multivariate Cox Models in Cancer Patients.Phenotype (cells/μL) P value Lymphocytes 0.2382 Granulocytes 0.0611Monocytes 0.0563 CD4 0.2206 CD8 0.8504 Regulatory T cells 0.9381CD14+HLA-DRlo/neg monocytes 0.0348 CD86+ monocytes 0.2405 B cells 0.9646NK cells 0.9071

TABLE 2 Ovarian cancer patient characteristics. Age Diagnosis 78 highstage, high grade cc/serous morphology 43 stage IC, grade 3 serous 63high stage, high grade primary peritoneal 57 mucinous cystadenoma 40mucinous borderline arising in muc. Adenoma 57 stage IIIC grade 3 serous68 stage IIIC high grade serous peritoneal primary 65 stage IIIC highgrade serous 55 stage IC grade 3 serous/clear cell mix 49 stage IC grade2 serous fallopian tube 53 stage 3C endo/clear cell mix 63 stage IIICserous primary peritoneal 58 stage IIIC high grade serous 29 stage 3Agrade 3 clear cell 66 stage 4 high grade serous peritoneal primary 51stage 3B high grade serous/endometrioid mix 58 stage 3C grade 3 serous

Flow Cytometry of Whole Blood

Peripheral blood was used as the source for antibody staining asdescribed elsewhere (Gustafson et al., Neuro Oncol. 12:631 (2010); andAppay et al., J. Immunol. Methods., 309:192 (2006)). Immune markersidentified included granulocytes, lymphocytes, monocytes (identified byforward and side scatter), CD3⁺ T cells, CD19⁺ B cells, (CD56⁺ ) NKcells, CD4⁺ T cells, regulatory T cells (CD4⁺CD25⁺CD127^(lo)), CD86⁺total monocytes, and CD14⁺HLA-DR^(lo/neg) immunosuppressive monocytes.Antibody reagents are listed in Table 3. Becton Dickinson TruCount™tubes were used to collect cell counts/μL of blood for T, B, and NKcells. The remaining markers were measured as a percent of these cellsby adding fluorochrome-conjugated antibodies that were directly added to50-100 μL of whole blood and incubated for 15-20 minutes at roomtemperature in the dark. RBCs were lysed with BD FacsLysis Solution(Becton Dickinson) per the manufacturer's instructions. Cells werecentrifuged, washed with PBS, and fixed in 4% para-formaldehyde. Datawere acquired on a Becton Dickinson FACSCalibur™ flow cytometercalibrated the day of use and analyzed with Cell Quest, Multiset (BectonDickinson), and/or Flowjo (Ashland, Oreg.) software. Descriptions ofgating strategies are listed in Table 4. The cell counts ofgranulocytes, lymphocytes, and monocytes were combined in each profile,and the average was plotted as pie graphs to represent the totalpopulation of circulating immune cells. The cell counts of CD4⁺ cells,CD8⁺ cells, B cells, NK cells, regulatory T cells, and CD14⁺ HLA-DR⁺monocytes, and HLA-DR^(lo) monocytes were combined, and the averageplotted as a pie graph to represent the total circulating mononuclearcells. CD8 cells were reported as the difference of CD3 and CD4 cells.CD14⁺ HLA-DR⁺ and HLA-DR^(lo) monocytes were calculated from thepercentage of CD14⁺ monocytes of total monocytes cell count (byforward/side scatter) and multiplied by the percentage of HLA-DR⁺ andHLA-DR^(lo) cells.

TABLE 3 Immune Phenotyping Reagents. Reagents/Antibodies Company Catalognumber FACS ™ Lysing Solution BD 349202 Trucount ™ tubes BD 340334 BDMultitest BD 340500 CD3/CD16⁺/CD56/CD45/CD19 CD127 PE BD 557938 CD4PerCP BD 340671 CD25 APC BD 555434 CD3 FITC BD 349201 CD3 PE BD 340662CD3 PerCP BD 340663 CD3 APC BD 340440 CD4 FITC BD 555346 CD8 PE BD340046 CD28 APC BD 559770 CD152 (CTLA4) PE BD 555853 CCR7 FITC R&DSystems FAB197F CD62L APC BD 559772 CD45RO BD 347967 CD8 PerCp BD 347314IgG FITC BD 349041 IgG PE BD 340761 IgG PERCP BD 349044 IgG APC BD340754 CD14 APC BD 555399 HLA-DR PerCP BD 347364 CD80 FITC BD 555683CD86 PE BD 555658 Lineage FITC BD 340546 CD33 APC BD 551378 CD16 PEeBioscience 12-0168-73

TABLE 4 Gating instructions for selected immune phenotypes (26 tubes).Phenotype Antibodies Gating strategy Granulocytes, Gate total leukocytesLymphocytes, Gate G, L, M by forward and side scatter. MonocytesRegulatory CD 4, CD3, CD127, and Gate lymphocytes T-cells CD25 PlotCD4/forward scatter - Gate CD4⁺ Plot CD 25 vs. CD127 Co- CD28, CD4, CD8,and Gate lymphocytes Stimulatory CTLA 4 Plot CD4/forward scatter - GateCD4⁺ and Inhibitory Plot CD28 vs. CTLA4 molecules on Plot CD8/forwardscatter - Gate CD8⁺ T-cells Plot CD28 vs. CTLA4 Central and CD4, CD45RO,CD62L, Gate lymphocytes Effector and CCR7 Plot CD4/CD45RO - GateCD4⁺/CD45RO⁺ Memory CD4 Plot CD62L vs. CCR7 Helper Cells Central andCD8, CD45RO, CD62L, Gate lymphocytes Effector and CCR7 Plot CD8/CD45RO -Gate CD8⁺/CD45RO⁺ Memory CD8 Plot CD62L vs. CCR7 Helper Cells Classical,CD14, CD16, and HLA- Gate monocytes Intermediate, DR Plot CD14/CD16 -and Non- Geometric mean (MFI) - HLA-DR Classical Plot HLA-DR vs. CD14Monocytes CD14+/HLA CD14 and HLA-DR Gate monocytes DR low/neg. GateCD14⁺ cells monocytes Plot CD14 vs. HLA-DR CD86+ CD86 Gate monocytesmonocytes Gate CD86⁺ cells Myeloid Lineage, CD33, and Gate peripheralblood mononuclear cells by derived HLA-DR forward and side scattersuppressor Gate lineage neg cells cells Plot CD33 vs. HLA-DR ReportCD33⁺/DR^(neg) as a percent of PBMC's

Multiparameter Analysis and Hierarchical Clustering

Immune marker values were either measured directly in cells/μL orconverted into cells/μL using the T, B, or NK counts. Mean values ofeach immune marker were determined using the values from 40 healthyvolunteers. Each immune marker for each individual (healthy volunteersand patients) was then normalized by dividing the individual value bythe mean value of healthy volunteers of that marker. The marker ratiosfor each volunteer and patient were imported into Partek Genomics Suite6.5 software (Partek Inc., St. Louis, Mo.) and log-transformed forhierarchical clustering. Hierarchical analysis was performed byunsupervised agglomerative Euclidean average linkage clustering.Principal component analysis was performed using the Scatter plot viewin the Partek program. Immune phenotypes as defined herein were thenumber and composition of circulating white blood cells within anindividual. An immune profile was a group of immune phenotypes(containing a minimum of seven members) with as few dendrogram branchesas possible. Additionally, all diseased members within a profile werecompared to diseased members of other profiles (unless indicated) todetermine profile differences.

Statistical Analyses

Values for subgroups of data were tested for statistical significanceusing the two-tailed non-parametric Mann-Whitney test for unpairedsamples, the non-parametric Spearman correlation test for correlativeanalyses, and the Fisher's 2×2 or 3×3 exact test for distributionbetween profiles. Cox models were used to identify prognostic factorsfor overall survival, where the models were adjusted for age andstratified by disease. The method of Contal and O'Quigley was used todetermine a best cut-point for the CD4+/CD14⁺HLA-DR^(lo/neg) monocyteratio (Contal and O'Quigley, Comput. Stat. Data Analysis, 30:253(1999)). Overall survival was evaluated using standard Kaplan-Meiermethods. All statistical analyses and graphs were performed using Prism,version 5.0 software (GraphPad Software, San Diego, Calif.) and SASsoftware (SAS Institute Inc., Cary, N.C.).

Identification of Distinct Immune Profiles Within Diseases

The number and relative composition of ten immune markers in peripheralblood of healthy volunteers (HV) and patients were assessed. Thesemarkers provided a comprehensive overview of the immune system withunambiguous gating strategies or had clearly defined functions relatedto immune suppression (Gustafson et al., Neuro. Oncol., 12:631 (2010);and Banham, Trends Immunol., 27:541 (2006)). To reduce thedimensionality of information and to cluster potentially similar immunephenotypes, cell counts were measured or calculated, normalized to thatof healthy volunteers, and analyzed using hierarchical clustering andprincipal component analysis.

Immune phenotypes were clustered within individual malignancies using HVas a control group for clustering. Unsupervised hierarchical clusteringwas performed on 27 glioblastoma (GBM) patients with 40 healthyvolunteers (FIG. 2A). Three high level profiles were identified. Profile1 contained 32 healthy volunteers, 5 dexamethasone-(DEX) treatedpatients, and 5 untreated GBM patients. Profile 2 contained 8 volunteersand 4 untreated patients. Profile 3 contained only 13 DEX-treated GBMpatients (p=<0.0001; Fisher's 3×3 exact test). The segregation of thesepatients based on DEX treatment agreed with the conventionally analyzedimmune markers of these patients where it was found that DEX treatmentassociated with decreased T cell numbers and loss of HLA-DR expressionon CD14⁺ monocytes (Gustafson et al., Neuro Oncol., 12:631 (2010)).Thus, this hierarchical clustering identified previously knowninformative subgroups.

Patients with NHL, renal cell carcinoma (RCC), and ovarian cancer (OVA)were analyzed in a similar manner with the same set of healthyvolunteers. Three profiles were assigned in NHL and RCC patients, and ineach case, the majority of patients clustered in profiles separate fromthose with the HV profile (Profile 1) (FIGS. 2B and 2C). The OVApatients clustered across profiles with relatively equal distributionwithin the healthy volunteers, suggesting that this patient group mostclosely resembled a “normal” immune profile (FIG. 2D).

The immune phenotypes of acute lung injury patients (ALI) were analyzed.Many critically ill ALI patients had an initially strongpro-inflammatory response but quickly fell into a prolongedanti-inflammatory state called immune paralysis (Rittirsch et al., Nat.Rev. Immunol., 8:776 (2008); and Moore et al., J. Trauma, 40:501(1996)). This patient population with or without concurrent sepsis wasused as an additional valuable test of this approach in a non-malignantcondition. There were three clearly identifiable profiles. Profile 1contained all volunteers and no ALI patients. Profile 2 contained 7 ALIpatients (5 septic). Profile 3 contained 16 ALI patients (11 septic)(FIG. 2E). Profiles 2 and 3 did not exhibit differences in thedistribution of septic patients. However, patients in Profile 2exhibited a lower survival rate than Profiles 1 and 3 in that 71% ofpatients in Profile 2 died from their condition, whereas only 19% ofpatients in Profiles 1 and 3 died (p=0.026). Taken together, theseresults indicate that hierarchical clustering can identify unique immuneprofiles for each disease group and that these profiles correlate withoverall immune status (such as GBM patients receiving immune suppressivetreatments) and clinical outcome (such as survival in ALI).

Identification of Distinct Immune Profiles Across Several Diseases

Assigned profiles within each cancer type and ALI differed regarding theunderlying immune characteristics (e.g., Profile 2 in GBM did not sharethe same immune markers or quantities of cells as Profile 2 in NHL orRCC). The power of hierarchical clustering to segregate informativelyimmune phenotypes was dependent on the number of the individuals used inthe analysis. To enhance the ability to identify immune profiles thatrepresent a common immune status, these assays were repeated in ananalysis that combined all healthy volunteers and patients.

Five major profiles with at least 10 patients were identified (labeled1-5) (FIG. 3A). Immune cell demographics are shown in FIG. 3B. Allhealthy volunteers were clustered within two immune profiles. Thedistribution of subjects among immune profiles was different for eachmalignancy or ALI yet some conditions had patients represented in eachimmune profile. These results suggest the existence of distinct profilesof immunity shared across diseases with disease specific variation inprofile distribution.

To confirm the uniqueness of each immune profile, the cell count datafrom each immune phenotype within each immune profile were used, and thevalues of each marker were compared to the values of the markers fromother profiles or to only the pooled healthy volunteers (FIG. 4A andTable 5). The values of the markers from patients within Profiles 1 and2 (where healthy volunteers typically segregate) were most similar tothe healthy volunteers' pooled group. When compared to the pooledhealthy volunteers, Profile 1 had fewer lymphocytes and elevatedCD14⁺HLA-DR^(lo/neg) monocytes. Profile 2 had fewer monocytes and fewerCD14⁺HLA-DR^(lo/neg) monocytes when compared to the pooled healthyvolunteers' profile. Profile 3 had elevated granulocytes, monocytes, andlymphocytes (mainly the T cell compartment) and elevated regulatory Tcells and CD14⁺HLA-DR^(lo/neg) monocytes. Profile 4 had elevatedgranulocytes and monocytes, but decreased lymphocytes (including T, B,and NK cells), decreased CD4⁺ T cells, and elevated CD14⁺HLA-Dr^(lo/neg)monocytes. Abnormally low monocytes and lymphocytes including CD4⁺ Tcells were present in patients in Profile 5.

TABLE 5 P values for differences of immunophenotypes compared acrossprofiles for FIG. 4A. 1 2 3 4 5 Granulocytes HV 0.0093 ns <0.0001<0.0001 ns 1 0.0414 <0.0001 <0.0001 ns 2 0.0009 0.0002 ns 3 ns 0.0013 40.0015 Lymphocytes HV 0.0406 ns 0.0319 <0.0001 <0.0001 1 0.404 0.0011<0.0001 <0.0001 2 ns <0.0001 <0.0001 3 <0.0001 <0.0001 4 0.0371Monocytes HV ns 0.0021 <0.0001 0.0018 <0.0001 1 0.0006 <0.0001 0.0010<0.0001 2 <0.0001 0.0001 0.0117 3 0.0071 <0.0001 4 <0.0001 T cells HV nsns 0.0270 <0.0001 <0.0001 1 ns 0.0018 <0.0001 <0.0001 2 ns <0.0001<0.0001 3 <0.0001 <0.0001 4 0.0359 B cells HV ns ns ns <0.0001 <0.0001 1ns 0.0393 <0.0001 <0.0001 2 ns 0.0057 0.0005 3 0.0003 0.0003 4 ns NKcells HV ns ns ns <0.0001 0.0002 1 ns ns <0.0001 0.0002 2 ns 0.00020.0011 3 0.0012 0.0072 4 ns CD4⁺ T cells HV 0.0274 ns ns <0.0001 0.00021 ns 0.0016 <0.0001 0.0002 2 ns <0.0001 0.0001 3 <0.0001 <0.0001 4 nsRegulatory T cells HV ns ns .0050 <0.0001 0.0146 1 ns 0.0016 <0.00010.0074 2 ns <0.0001 0.0001 3 <0.0001 <0.0001 4 ns CD14⁺HLA- DR^(lo/neg)monocytes HV 0.0022 <0.0001 <0.0001 <0.0001 ns 1 <0.0001 <0.0001 <0.0001ns 2 <0.0001 <0.0001 0.0001 3 ns <0.0001 4 <0.0001 CD86⁺ monocytes HV ns0.0060 <0.0001 ns <0.0001 1 0.0010 <0.0001 ns <0.0001 2 <0.0001 0.00800.0061 3 0.0019 <0.0001 4 <0.0001 ns = p > 0.05

To evaluate the differences of immune profiles in relative quantities,the values from each immune phenotype were obtained, and the data werecompared with values from the other profiles or the pooled healthyvolunteers (FIG. 4B). This analysis confirmed that the profiles groupedsubjects with similar immune profiles. Importantly, this method ofanalysis demonstrated that the profiles were identifying subjects withsimilar immune statuses representing both absolute and relativedifferences in key immune cells.

The data allowed the reconstruction of the average immune phenotype thatexists within a profile. The average values of each of the immunemarkers (cells/μL) within an immune profile were used to reconstruct thecomposition of the average immune phenotype within that profile andplotted as a pie chart (FIG. 4C) for the entire leukocyte compartmentand peripheral blood mononuclear cells (PBMC). The size of the pie chartreflected the relative quantity of cells per fixed unit of bloodrelative to Profile 1. For example, Profile 3 had almost twice as manytotal leukocytes as Profile 1 (p<0.0001 and FIG. 5), and Profile 4 hadover 1.5 times that of Profile 1 (p<0.0001 and FIG. 5). This analysisallowed visualization of both relative and absolute values. Thedirection of the absolute change in total cells/μL was similar in allprofiles except profile 4 where the leukocyte population increased whilethe PBMC population decreased. The magnitude of the difference alsodiffered in the PBMC pools with Profile 3 having 1.5 times the amount ofPBMCs than Profile 1 (p<0.0001 and FIG. 5) while Profile 5 had less thanhalf of PBMCs than Profile 1 (p<0.0001 and FIG. 5). These resultssuggested that there exists peripheral blood immune profiles sharedacross disease states and that these profiles consisted of changes inthe absolute and relative quantities of individual white blood cells.

Immune Profiles Correlate with Patient Outcome

Immune profiles were predictive of survival in ALI (FIG. 2E). To see ifimmune profile predicted survival across cancer diagnosis, the data setswere used with survival data (GBM, NHL, and RCC patients). Patients werecategorized by immune profile groups rather than cancer type, adjustingfor age and cancer type. To provide sufficient samples, profiles weregrouped as to those most closely resembling normal immune system andthose that do not. As healthy volunteers were only grouped into Profiles1 and 2, the patients found in these two immune profiles were pooled,and the overall survival of these patients was compared to thosepatients in the three remaining profiles (FIG. 6). The median overallsurvival of patients in Profile 1 and 2 (915 days, n=42) was almost twoand a half times as long as those in the other immune profiles (379days; n=34, p=0.009). In contrast, immune profiles identified withindisease groups did not associate with survival (FIGS. 7A-D). It is clearthat the unbiased approach presented herein segregated patients basedsolely on an unbiased immune status to identify those with the worstprognosis independent of underlying disease.

Identification of Related Immune Markers Using Hierarchical Clustering

In addition to clustering individuals into immune profiles, hierarchicalclustering identified immune markers related by their common presenceacross immune profiles. The common segregation of two immune markers wasconsidered an immunological node. A subset of patients had been typedwith a total of 23 immune markers. The previous analysis was repeatedusing these expanded immune markers to look at their potentially relateddistribution. Some relationships observed were expected including thosewhere a marker was a large component of another marker such as T cellsand lymphocytes, CD4⁺ T cells with CD28⁺CD4⁺ T cells and central memoryCD4⁺ T cells (CD4Tcm), and CD14⁺CD16⁻ classical monocytes with CD86⁺monocytes (FIG. 5A). Some relationships were new such as granulocyteswith CD14⁺HLA-DR^(lo/neg). Likewise, some considered related were notfound together such as Tregs independent of other CD4⁺ cells, and CTLA4⁺T cells independent of T cells. Lineage-HLA-DR-CD33⁺ myeloid derivedsuppressor cells (MDSCs) clustered independently of both granulocytesand monocytes, suggesting independent regulation. Thus, this analysisproduced correlative evidence of similar or disparate regulation ofcertain white blood cells in humans.

Two markers clustering largely to themselves were the CTLA4⁺ T cells,and the CD14⁺HLA-DR^(lo/neg) phenotype. CD14⁺HLA-DR^(lo/neg) monocyteswere identified as a predictor of poor prognosis and powerful mediatorsof immune suppression in GBM (Gustafson et al., Neuro. Oncol., 12:631(2010)), NHL (Lin et al., Blood, 117:872 (2011)), chronic lymphocyticleukemia (CLL) (Gustafson et al., Br. J. Haematol., 156:674 (2012)), andRCC. This phenotype had one of the largest degrees of change in bothrelative and absolute terms in the analysis provided herein (FIG. 4 andTable 6). To investigate if this segregation could identify interestingcorrelations within the immune system, the complete cohort of patientsand HV were used to perform correlation analyses with two closelyrelated markers and one marker segregating at a distant to theCD14⁺HLA-DR^(lo/neg) phenotype. CD14⁺HLA-DR^(lo/neg) monocyte cellcounts positively correlated with total monocyte and granulocyte counts,markers that closely segregated to the CD14⁺HLA-DR^(lo/neg) (FIG. 8B).In earlier analyses, CD4⁺ T cell counts were inversely correlated to thepercentage of CD14⁺ HLA-DR^(lo/neg) monocytes (of total CD14+ monocytes)in GBM patients. Here, in a larger cohort of subjects including bothvolunteers and cancer patients, CD4⁺ cells segregated distally from theCD14⁺ HLA-DR^(lo/neg) phenotype and were inversely correlated to thepercentage of CD14⁺HLA-DR^(lo/neg) monocytes (p<0.001; Spearmanr=−0.3244). The data herein suggested certain markers such asCD14⁺HLA-DR^(lo/neg) monocytes may be largely independently regulatedand are an important component of the leukocyte population key to thecharacterization of the overall status of the immune system. It alsoidentified key inverted relationships that might lead to improveddescription of the immune system with seemingly unrelated immunephenotypes.

TABLE 6 Overall Survival Multivariate Cox Models in Cancer Patients.Phenotype (cells/μL) P value Lymphocytes 0.2382 Granulocytes 0.0611Monocytes 0.0563 CD4 0.2206 CD8 0.8504 Regulatory T cells 0.9381CD14⁺HLA-DR^(lo/neg) monocytes 0.0348 CD86⁺ monocytes 0.2405 B cells0.9646 NK cells 0.9071The CD4⁺/CD14⁺HLA-DR^(lo/neg) Ratio is a Prognostic Biomarker in CancerPatients

The inverted relationship between CD14⁺HLA-DR^(lo/neg) and CD4⁺ cellsidentified above was chosen to determine if selected but disparateinformative markers could describe immune status. Analysis of theindividual markers did not identify a survival difference althoughlymphocytes counts have proven to be useful prognostic markers in somecancer populations (Porrata et al., Biology of Blood& MarrowTransplantation, 14:807 (2008); and Ege et al., British Journal ofHaematology, 141:792 (2008)). To describe the contribution of bothvariables, the ratio of the number of CD4⁺ T cells to the number ofCD14⁺HLA-DR^(lo/neg) monocytes (cells/μL) was calculated. The 40 healthyvolunteers had a mean CD4+/CD14⁺HLA-DR^(lo/neg) ratio of 39.8 (median22.5) with a minimum of 3.9. The GBM, NHL, and RCC patients weresubgrouped into those with high or low ratio, with a cut-point ratio of2.0. The overall survival of GBM, NHL, and RCC patients with high andlow ratio was analyzed using multivariate analysis to control for ageand disease type. The median overall survival for patients with a ratioabove 2.0 was 30 months (n=68) compared to 9 months for patients with alow ratio (n=39; p=0.006 by multivariate analysis) (FIG. 8C). Theseresults demonstrate that this ratio is a strong predictive biomarker forrisk stratification and prognosis.

This document provides methods and materials to describe comprehensivelythe immune system based on whole blood flow cytometry, determining thenumber of cells/μL for the major leukocyte components, and hierarchicalclustering. Bioinformatics analysis was used to cluster individuals intoimmune profiles. The power of bioinformatics to cluster by similaritywas related to the number of samples included in the analysis, thedisease stratification, and how consistent all members within a profileare. The technical approach used (e.g., whole blood flow cytometry withcell quantitation) combined with a consensus antibody and gatingstrategy can be used to establish a comprehensive analysis of peripheralblood immunity with thousands of patients and healthy volunteers toextract relationships between immunity and disease.

The results provided herein involved (1) direct staining of freshun-manipulated whole blood, (2) the use of an unbiased approach lookingat multiple immune markers, (3) reporting cell populations as cellcounts (cells/μL) to enumerate populations more accurately, and (4) adata set of healthy volunteers to determine the degree of change ofimmune markers. By combining these principles with gating strategies andpatient health annotation, a large multi-institutional database can beestablished to provide a powerful resource for assessing humanimmunology.

Example 2 Immune System Profiles

The number of granulocytes, lymphocytes, monocytes, T cells, B cells, NKcells, CD4⁺ lymphocytes, regulatory T cells, CD14⁺HLA-DR^(lo/neg)monocytes, and CD86⁺ monocytes per μL of whole blood was determined for40 healthy volunteers (HV), 97 cancer patients, and 23 patients withacute lung injury and used to identify the mean ±1 standard deviationfor immune system profiles 1, 2, 3, 4, and 5 (Table 7).

TABLE 7 Mean immune marker values in cells/μL of whole blood. ImmuneMarker HV Profile 1 Profile 2 Profile 3 Profile 4 Profile 5 Granulocytes4679 ± 1610 6135 ± 2745 5508 ± 4911 12808 ± 7423  12402 ± 6469  6644 ±3252 Lymphocytes 1722 ± 512  1579 ± 613  1948 ± 606  2242 ± 722  769 ±250 645 ± 399 Monocytes 497 ± 148 533 ± 172 367 ± 105 1085 ± 375  784 ±410 253 ± 151 T cells 1258 ± 387  1158 ± 470  1397 ± 522  1679 ± 638 563 ± 220 456 ± 375 B cells 243 ± 128 222 ± 166 225 ± 134 347 ± 209 109± 75  81 ± 87 NK cells 222 ± 102 200 ± 91  326 ± 285 215 ± 125 97 ± 72107 ± 87  CD4⁺ T cells 921 ± 317 825 ± 350 885 ± 282 1201 ± 455  352 ±156 381 ± 480 Regulatory T cells 28 ± 17 32 ± 23 30 ± 15 55 ± 32 13 ± 8 21 ± 24 CD14⁺HLA-DR^(lo/neg) 51 ± 36 97 ± 88 10 ± 7  595 ± 312 426 ± 31872 ± 58 monocytes CD86⁺ monocytes 455 ± 126 483 ± 157 336 ± 104 962 ±351 613 ± 403 195 ± 155

In addition, the measured cell numbers were used to determine the meanratio of each immune marker (i.e., cell type) in each profile comparedto the healthy volunteer cohort±one standard deviation (Table 8).

TABLE 8 Mean immune marker ratio values. Immune Marker HV Profile 1Profile 2 Profile 3 Profile 4 Profile 5 Granulocytes 1.00 ± 0.34 1.30 ±0.59 1.23 ± 0.29 2.73 ± 1.59 2.65 ± 1.38 1.42 ± 0.70 Lymphocytes 1.00 ±0.30 0.92 ± 0.35 1.10 ± 0.38 1.30 ± 0.42 0.45 ± 0.15 0.37 ± 0.23Monocytes 1.00 ± 0.30 1.07 ± 0.35 0.75 ± 0.24 2.18 ± 0.76 1.58 ± 0.820.51 ± 0.30 T cells 1.00 ± 0.31 0.92 ± 0.37 1.08 ± 0.44 1.34 ± 0.51 0.45± 0.17 0.36 ± 0.30 B cells 1.00 ± 0.53 0.92 ± 0.68 0.91 ± 0.56 1.43 ±0.86 0.45 ± 0.31 0.33 ± 0.36 NK cells 1.00 ± 0.46 0.91 ± 0.41 1.43 ±1.31 0.97 ± 0.56 0.44 ± 0.32 0.49 ± 0.39 CD4⁺ T cells 1.00 ± 0.35 0.90 ±0.38 0.91 ± 0.29 1.31 ± 0.49 0.38 ± 0.17 0.41 ± 0.52 Regulatory T cells1.00 ± 0.58 1.13 ± 0.84 1.16 ± 0.51 1.96 ± 1.14 0.45 ± 0.28 0.74 ± 0.87CD14⁺HLA- 1.00 ± 0.73 1.89 ± 1.73 0.25 ± 0.19 11.66 ± 6.13  8.35 ± 6.231.42 ± 1.13 DR^(lo/neg) monocytes CD86⁺ monocytes 1.00 ± 0.28 1.06 ±0.34 0.75 ± 0.26 2.11 ± 0.77 1.35 ± 0.89 0.43 ± 0.34

OTHER EMBODIMENTS

It is to be understood that while the invention has been described inconjunction with the detailed description thereof, the foregoingdescription is intended to illustrate and not limit the scope of theinvention, which is defined by the scope of the appended claims. Otheraspects, advantages, and modifications are within the scope of thefollowing claims.

1. (canceled)
 2. A method for treating a human having renal cellcarcinoma, wherein said method comprises: (a) performing flow cytometryusing a blood sample obtained from a human having renal cell carcinomato identify said human as having an immune system profile of apopulation of healthy humans, and (b) administering surgery, IL-2, orcryotherapy to said human.
 3. The method of claim 2, wherein said methodcomprises performing flow cytometry using said blood sample to identifysaid human as having said immune system profile.